Allows diffusion of imaging research in a clinical oncology setting and provides tools for end-to-end diffusion image analysis as well as interoperation with clinical imaging systems. SlicerDMRI is a suite of open-source software tools for diffusion magnetic resonance imaging (dMRI) research. The software is built upon and integrated with 3D Slicer, an NIH-supported open-source platform for medical image computing. SlicerDMRI is used for both neuroscience research and cancer imaging research.
Allows to study diffusion Magnetic Resonance Imaging (MRI) data. Dipy is a program allowing users to share their code and experiments. One of its objectives is to provide transparent implementations for all the different steps of the dMRI analysis with a uniform programming interface. It implements two interfaces for probabilistic Markov fiber tracking: (1) it allows the user to provide the distribution evaluated on a discrete set of possible tracking directions, and (2) it accommodates tracking methods where the fiber orientation distribution function (fODF) cannot be easily computed.
Constructs deformable brain images. HAMMER can reveal geometric characteristics of the underlying anatomical structures. It employs a hierarchical deformation mechanism and an attribute vector to work. This tool can reflect the geometric properties of the underlying structure from a local scale, to a global scale that reflected spatial relationships with more distant surface points.
Allows (pre-)processing and visualization of diffusion magnetic resonance imaging (dMRI) data. Braintome DiffusionKit includes all of the required steps from the original DICOM images to brain anatomical network construction and user-friendly operation and visualization. The software includes data format conversion, preprocessing, local modeling and reconstruction, fiber tracking, and fiber statistics.
Consists of a non-parametric diffeomorphic registration algorithm. Diffeomorphic Demons is an optimization procedure on the entire space of displacement fields. This method can provide non-parametric free-form diffeomorphic transformations.
A set of free and extensible open source neuroimaging tools written in Python. The key components of the toolkit are as follows: (1) The Connectome File Format is an XML-based container format to standardize multi-modal data integration and structured metadata annotation. (2) The Connectome File Format Library enables management and sharing of connectome files. (3) The Connectome Viewer is an integrated research and development environment for visualization and analysis of multi-modal connectome data. The Connectome Viewer's plugin architecture supports extensions with network analysis packages and an interactive scripting shell, to enable easy development and community contributions. Integration with tools from the scientific Python community allows the leveraging of numerous existing libraries for powerful connectome data mining, exploration, and comparison.